Saeede Nazari Goldar

Bio: Saeede Nazari Goldar is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Exoskeleton & Inverse kinematics. The author has an hindex of 1, co-authored 2 publications receiving 20 citations.

Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation

Abstract: In this paper, mechanical design and control of an exoskeleton robot for shoulder rehabilitation after stroke are presented. Initially, mechanical design of a new 3 degrees of freedom (DOF) exoskeleton robot for shoulder joint rehabilitation is presented. All robot measurements are based on the properties of upper limb of an adult person. A new open circular mechanism is proposed for the third joint. Afterwards, direct and inverse kinematics, Jacobian matrix, singular points, and dynamics of the robot are presented. In order to study the ability of the robot to follow the optimized trajectories, sliding mode controller (SMC) is proposed to track desired trajectories. In most rehabilitation robots, the attention is on robot's mechanical design, so linear controllers are used to control the robot. However, rehabilitation robots are non-linear in nature and non-linear control methods are required that can reject uncertainties and are resistant to parameter changes. SMC is robust due to its nonlinear nature, and can reject uncertainties and disturbances applying on the system such as patient's hand tremor. The parameters of the SMC are tuned using Genetic Algorithm (GA). The main advantage of this robot compared to similar systems are being low weight, having a special mechanism for third joint that solves the known issues associated with long wiring and closed mechanisms, allowing translational degrees of freedom of the shoulder, ease of use, comfort for the patient and the tracking performance of the controllers.

Methodology for optimization of pouring variables using bottom pour for low masses of SS304 (<300 kg) in investment casting process (case study)

Abstract: Bottom pour ladles with stopper rod systems are commonly used in the metal casting industry. However, stopper rod bottom-pouring systems have not yet been developed for the lower thermal masses of ...

Biomechanics And Motor Control Of Human Movement

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Adaptive Neural Network Fast Fractional Sliding Mode Control of a 7-DOF Exoskeleton Robot

Abstract: To rehabilitate individuals with impaired upper limb (UL) functions due to neurological disorders, this research focuses on trajectory tracking control (representing passive rehabilitation exercise) of a 7 DOFs exoskeleton robot named ETS-MARSE. It is a redundant type of robotic manipulator having a very complex structure which is designed based on human UL joint articulations. The exoskeleton is constantly encountered with external disturbances and unknown dynamics such as friction forces, and backlash which is hard to model. Moreover, this type of robot needs to deal with the unknown dynamics of a wide range of subjects with different degrees of UL impairments. Therefore, to deal with this modeling uncertainty, in this paper we propose a novel adaptive neural network fast fractional integral terminal sliding mode control (ANFFITSMC) approach to maneuver the ETS-MARSE to provide passive arm movement therapy. To address the chattering phenomena which are observed in the fast fractional integral terminal sliding mode control (FFITSMC), a new adaptive radial basis function neural network (ARBFN) is incorporated with the FFITSMC. The Lyapunov theory is used in order to prove the stability of the proposed controller. Simulation results validated the efficient performance of the ANFFITSMC in terms of chattering reduction and trajectory tracking.

Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation

Abstract: This paper investigates the control of a 5-DOF upper-limb exoskeleton robot used for passive rehabilitation therapy. The robot is subject to uncertain dynamics, disturbance torques, unavailable full-state measurement, and different types of actuation faults. An adaptive nonlinear control scheme, which uses a new reaching law-based sliding mode control strategy, is proposed. This scheme incorporates a high-gain state observer with dynamic high-gain matrix and a fuzzy neural network (FNN) for state vector and nonlinear dynamics estimation, respectively. Using dynamic parameters, the scheme provides an efficient mean for simultaneously tackling the effects of FNN approximation errors, disturbance torques and actuation faults without any prior bounds knowledge and fault detection and diagnosis components. Using simulation results, it is shown that with the presented scheme, faster response, fewer oscillations during transient phase, good tracking accuracy, and chattering-free control torques with lower amplitudes are obtained.

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review.

Abstract: Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

A Novel Exoskeleton with Fractional Sliding Mode Control for Upper Limb Rehabilitation

Abstract: The robotic intervention has great potential in the rehabilitation of post-stroke patients to regain their lost mobility. In this paper, firstly, we present a design of a novel, 7 degree-of-freedom (DOF) upper limb robotic exoskeleton (u-Rob) that features shoulder scapulohumeral rhythm with a wide range of motions (ROM) compared to other existing exoskeletons. An ergonomic shoulder mechanism with two passive DOF was included in the proposed exoskeleton to provide scapulohumeral motion with corresponding full ROM. Also, the joints of u-Rob have more range of motions compared to its existing counterparts. Secondly, we propose a fractional sliding mode control (FSMC) to control u-Rob. Applying the Lyapunov theory to the proposed control algorithm, we showed the stability of it. To control u-Rob, FSMC has shown effectiveness to handle unmodeled dynamics (e.g. friction, disturbance, etc.) in terms of better tracking and chatter compared to traditional SMC.